# Tabular examples

These examples explain machine learning models applied to tabular data. They are all generated from Jupyter notebooks available on GitHub.

## Tree-based models

Examples demonstrating how to explain tree-based machine learning models.

- Basic SHAP Interaction Value Example in XGBoost
- Catboost tutorial
- Census income classification with LightGBM
- Census income classification with XGBoost
- Example of loading a custom tree model into SHAP
- Explaining a simple OR function
- Explaining the Loss of a Tree Model
- Fitting a Linear Simulation with XGBoost
- Force Plot Colors
- Front page example (XGBoost)
- League of Legends Win Prediction with XGBoost
- NHANES I Survival Model
- Speed comparison of gradient boosting libraries for shap values calculations
- parameters
- Python Version of Tree SHAP
- Scatter Density vs. Violin Plot
- Understanding Tree SHAP for Simple Models

## Linear models

Examples demonstrating how to explain linear machine learning models.

## Neural networks

Examples demonstrating how to explain machine learning models based on neural networks.

## Model agnostic

Examples demonstrating how to explain arbitrary machine learning pipelines.

- Census income classification with scikit-learn
- Diabetes regression with scikit-learn
- Iris classification with scikit-learn
- SHAP Values for Multi-Output Regression Models
- Create Multi-Output Regression Model
- Get SHAP Values and Plots
- Reference
- Simple Boston Demo
- Simple Kernel SHAP
- How a squashing function can effect feature importance